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To Suzie,

Milton, Oskar, and Nash—felinus economicus

Acknowledgments

This project could never have been completed without the support and assistance of my wife, Suzie. She has helped in countless and immeasurable ways. Families play an integral role in dreams, aspirations, and accomplishments. My family has paved a clear path for any and every ambition that I could possibly have. They are indubitably responsible for all of my successes. My mother and father have always instilled the importance of education, hard work, free thinking, and discipline. For that I could never thank them enough. A special thanks to my brother Robert and to my in-laws, Richard and Nancy McCabe.

Educators can have a profound influence on one’s life. My life—as well as this project—was no exception in the way of encouragement and wisdom disseminated by those in academia. Professors and mentors have helped, not only in the understanding of some of the roles that economic indicators and statistics assume, but as counselors to a not-so-quick-to-learn student. They include, and are not limited to, David W. Ring, William O’Dea, Robert Carson, and Thomas Gergel of the State University of New York at Oneonta. Prof. Ring taught me to work hard, Prof. Carson to look at each situation from alternative perspectives, and Prof. Gergel to have a passion about whatever task I might take on. I practice these three lessons every day of my life.

At John Wiley & Sons I must thank Meg Freeborn, Kimberly Monroe-Hill, and Evan Burton. Meg, thanks for your diligent efforts and for identifying the previously unnoticed errors of the first two editions. Kimberly, a big thanks for fixing and polishing up the final product. Evan, your professionalism is second to none; I hope we work together on many projects in the future.

The list of professional associates who have helped me with this project could easily take up the entire book, but some individual recognition is essential. In my 25-plus years of work experience on Wall Street I have never been associated with a more professional firm than Bloomberg LP, particularly Bloomberg News. It is for this reason that I formally decided to join the Multimedia Group in the fall of 2009.

To the hundreds of friends, acquaintances, and colleagues at the Bloomberg offices all around the world, thanks for all your help and input into this project. You are unaware of how helpful you have been. I especially wish to thank Kevin Tynan—the best auto analyst on Wall Street, Vinny DelGiudice, Vince Golle, David Yanofsky, Tom Keene, Sandra Mosiello, Tony Bolton, David Nogeroul, Alex Tanzi, Michael McKee, Sara Eisen, Rachel Wehrspann, Caroline Baum, Mark Crumpton, Ellen Braitman, Dominick Chu, Janice Slusarz, and Jackie Jozefek.

I must extend my thanks to my new team at the Bloomberg Economics BRIEF newsletter group, including Ted Merz—a true guiding light and the ultimate voice of reason. To my editors, Kevin Depew and Jennifer Rossa, I continue to improve because of your tremendous efforts. To the economists at the Economics BRIEF, Michael McDonough, David Powell, Joseph Brusuelas, and Niraj Shah, I can’t think of a better and more intelligent bunch of guys to work with. (Actually, I can name a few thousand, I’m just being nice.) Special thanks to Michael Nol, James Crombie, Aleks Rozens, and Nick Ferris—even though I can’t help but think I am entitled to some royalties for entertaining you four on a daily basis. Seriously.

Acknowledgment wouldn’t be complete without special thanks to those who have helped with this project from the first edition: Charles Gilbert and Michele Johnson (Board of Governors of the Federal Reserve System); Lynn Franco (the Conference Board); Richard Deitz (Federal Reserve Bank of New York); Guhan Venkatu (Federal Reserve Bank of Cleveland); Scott Scheleur (Service Sector Statistics Division, U.S. Census Bureau); Kristen Kioa (Institute for Supply Management); Jeannine Aversa and Marty Crutsinger (Associated Press); Garrett Bekker; Steve Berman (U.S. Department of Commerce, Bureau of Census); Barbara Hagenbaugh, Sue Kirchhoff (USA Today); Jason Hecht (Ramapo College); David Jozefek; Thomas Feeney (Shippensburg University); Jeffrey J. Junior; Stacie Negas and Bob Israel; and Joe Pregiato (Arbor & Ivy).

And a very special thanks to Lakshman Achuthan and Anirvan Banerji from the Economic Cycle Research Institute (ECRI) in New York. They oversee some of the finest economic indicators on the Street today.

Any errors or oversights that may exist in this book were not intentional and are not the fault of any of those individuals named above.

Introduction

Investing without understanding the economy is like taking a trip without knowing anything about the climate of your destination. Inclement weather can wreak havoc on a vacation, especially if it involves outdoor activities. Just so, putting hard-earned money into the stock or bond market when economic conditions are unfavorable can destroy financial plans for a comfortable retirement, a new house, or a child’s college education.

No one understands this better than Wall Street investment banks, brokers, and research institutions. All of these have adopted a top-down approach to securities analysis that begins with a forecast of the general economic climate, including interest rate projections, currency forecasts, and estimates of domestic and foreign economic growth. In this, they are following one of the precepts laid down by Benjamin Graham and David Dodd in their 1940 investors’ bible, Security Analysis: “Economic forecasts provide essential underpinning for stock and bond market, industry, and company projections.

You don’t need to manage millions or billions of dollars, however, to study economic conditions and plan your investment strategy accordingly. You can get much of the same information that Wall Street professionals use in their analyses from the business sections of the nation’s newspapers, magazines, and evening news programs. Furthermore, you don’t need a degree in economics or mathematics to interpret this information. In fact, many graduates of such programs at the nation’s top universities find themselves entirely unprepared for the real world of finance. This book attempts to bridge the wide gap between the sometimes mind-numbing theories of textbook economics—the principles that are taught on college campuses across the country—and the everyday world of Wall Street. It does so by focusing on a dozen economic indicators and several others from the fixed-income and commodity markets that are among the most important of any analyst’s or economist’s tools. Understanding these indicators will make the study of economics more palatable and exciting.

Over the past century, thousands of economic indicators have emerged, predicting everything from the demand for gasoline to the size of harvests. Some are more fun than functional, such as those claiming links between stock performance for the year and which conference—the NFC or the AFC—wins the Super Bowl, or whether women’s hemlines rise to midthigh or fall to midcalf. Other indicators are more serious, solidly based in economic observations. These range from the arcane—such as the indicator connecting the production level of titanium dioxide, an ingredient of pigments used in paints and plastics, with the demand for building materials—to the commonsensical. The price of copper, used in wiring and many other construction elements, for instance, has a clear relationship to the pace of housing activity. The same could be said of economic growth and railroad car loadings, shipping container production, wooden pallet shipments, and the manufacture of corrugated boxboard and packaging, all of which are connected with transporting freight or manufactured goods.

Over time, economists have weeded out the least successful indicators, based on the most dubious relationships, to arrive at a core of about 50 consistently reliable ones. This book presents the dozen or so that are must-haves in any analytical toolbox. Virtually all Wall Street economists use these indicators in their analyses and their writings. Federal Reserve officials conduct monetary policy based on the trends that these indicators project. They are also considered must-haves in the sense that they are among the most accurate at depicting economic relationships as well as attendant market-movability. That is, each of these indicators at one time or another typically figures among the top-tier factors that can presage big swings in the financial markets.

Some of the dozen-plus indicators discussed in this book are constructed by U.S. government agencies such as the U.S. Department of Commerce’s Census Bureau, the U.S. Department of Labor, and the Board of Governors of the Federal Reserve System. Others are the products of private organizations such as the Institute for Supply Management, the Conference Board, and the University of Michigan. Some have excellent predictive powers. Others reflect principally the current state of the economy, and still others highlight industries that might outperform and so help identify the likely path of economic activity. All have one thing in common, however: In one way or another, they all relate to the business cycle.

The Business Cycle

The business cycle is one of the central concepts in modern economics. It was defined by celebrated economists Arthur Burns and Wesley Mitchell in their pioneering 1946 study, Measuring Business Cycles, written for the National Bureau of Economic Research (NBER), which today is the official arbiter of the U.S. business cycle. According to Burns and Mitchell, the business cycle is “a type of fluctuation found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals, which merge into the expansion phase of the next cycle.”

No two business cycles are the same. As illustrated in , during the relatively short time that people have been measuring the U.S. economy, the length of expansions, from economic trough to peak, and of contractions, from peak to trough, has varied widely—although expansions, especially recently, generally have been longer and steadier than contractions. Expansions have ranged from 120 months (April 1991 to March 2001) to 10 months (March 1919 to January 1920), and downturns from 43 months (August 1929 to March 1933) to 6 months (February 1980 to July 1980). The amplitude of the peaks and troughs has also differed significantly from cycle to cycle.

 U.S. Business Cycle Durations

Source: National Bureau of Economic Research

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One way to think of the business cycle is as a graphical representation of the total economic activity of a country. Because the accepted benchmark for economic activity in the United States is currently gross domestic product (GDP), economists generally identify the business cycle with the alternating increases and declines in GDP. Rising GDP marks economic expansion; falling GDP, a contraction (see ). That said, the business cycle, as defined by Burns and Mitchell, can’t be fully captured by one indicator, even the nation’s GDP. Rather, it is a compendium of indicators that reflects various aspects of the economy.

 GDP and Highlighted Recessions

Source: U.S. Department of Commerce, Bureau of Economic Analysis; National Bureau of Economic Research

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Economic indicators are classified according to how they relate to the business cycle. Those that reflect the current state of the economy are coincident; those that predict future conditions are leading; and those that confirm that a turning occurred are lagging.

Indicators and the Markets

The organization responsible for an indicator generally distributes its report about an hour before the official release time to financial news outlets such as Bloomberg News, Dow Jones Newswires, Thomson Reuters, and CNBC. The reporters, who are literally locked in a room and not permitted to have contact with anyone outside, ask questions of agency officials and prepare headlines and analyses of the report contents. These stories are embargoed until the official release, at which time they are transmitted over the newswires to be dissected by the Wall Street community. Most Wall Street firms employ economists to provide live broadcasts of the numbers as they run across the newswires, together with interpretation and commentary regarding the likely market reaction. This task, known as the “hoot-and-holler” or tape reading, is among the most stressful performed by an economist. One slip-up can cost a trader or an entire trading floor millions of dollars.

The more an indicator deviates from Street expectations, the greater its effect on the financial markets. A 0.1 percent decline in retail sales, for example, might not move the markets much if economists were looking for a flat reading or a 0.1 percent rise. But if the consensus was for an increase of 0.7 percent, and instead the 0.1 percent decline hit the tape, the markets might well be rocked. That said, it is always prudent for traders and other market participants to keep apprised of what the Street expectations are for key economic indicators such as those covered here.

How to Use This Book

You’ve no doubt read in a paper or heard on television or the radio forecasts of economic expansion or recession. You also probably realize that the one is desirable and the other is not. But you may not know how the economists quoted came up with their predictions. Without this knowledge, how can you judge how well considered or rash they are—and whether to trust them in creating your investment strategy? This book seeks to help you form your own opinions about the possible direction of the economy and the markets and to decide how to act based on those opinions.

Each chapter corresponds to an indicator, beginning with the most comprehensive—the GDP and indices of leading, lagging, and coincident indicators—and continuing with those tied to particular aspects or segments of the overall economy, such as consumer prices, manufacturing, housing, and retail sales. Every chapter contains four principal sections: an introduction sketching out the major attributes of the indicator and its effect on the markets; a discussion of its origins and development; a description of how the relevant data are obtained, analyzed, and presented; and an explanation of how to incorporate these data into your investment process. The last section also contains at least one trick—involving either a little-known subcomponent of the indicator or a combination of subcomponents—that Wall Street economists use to get a clearer or more timely picture of business activity. At the end of each chapter is a listing of additional reading and resources, pointing those interested to references that discuss the relevant indicator in greater detail.

In putting what you learn from this book into practice, you might take some pointers from Wall Street. Just about every investment firm has a pre-market-opening meeting in which the day’s events and potential trading strategies are presented. This always includes a discussion of the economic indicators scheduled for release that day. No trader wants to be caught off guard by an unexpected market-moving release. For the same reason, many traders have on their desks calendars showing which economic release is scheduled for a particular day and indicating both the value or percentage change of the previous report and the Street’s estimates—highest, lowest, and consensus—for the upcoming one. That way, when the actual figure is released, they will know how it compares with expectations and can react accordingly.

Of course, no single economic indicator will tell you all you need to know about the current or future economic climate. Each has drawbacks and may send false signals because of unforeseen shocks, faulty measurements, or suspect collection processes. Piecing together the information from all of the indicators discussed in this book like tiles in a mosaic will give you a dynamic representation of the economy. But if you are truly serious about understanding the macroeconomic climate and individual industry conditions, you should also take advantage of the Securities and Exchange Commission’s Regulation Fair Disclosure of 2000, which mandates for individual investors the same access to companies’ quarterly earnings conference calls that professional analysts have.

These calls provide a great deal of insight into corporate spending plans, manufacturing and production activity, international conditions, pricing, and the general business climate. Especially informative are the announcements of industrial behemoths such as Alcoa, Boeing, Caterpillar, Cummins, Emerson Electric, Ford Motor Company, General Electric, Illinois Tool Works, Johnson Controls, and United Technologies. Many companies also offer slide presentations, handouts, and supplemental data with these quarterly presentations, which often provide even greater detail on their buying intentions, prospective employment changes, and any threats to performance that they foresee. There’s no cheaper and easier way to gather anecdotal evidence about business conditions. If you can’t listen in, the presentations are almost always archived on company web sites, from which they may be readily retrieved 24/7.

Who Can Benefit from This Book?

This book was written primarily for those traders and investors lacking a formal introduction to the most popular economic indicators on Wall Street. Just because an individual is entrusted with investing millions of dollars does not guarantee a practical command of economic indicators and their meaning for investment. When newly minted MBAs arrive on the trading floors of financial firms, for example, few are equipped with a complete appreciation of these indicators—no matter from which institution that degree has come. My years of experience on a few of the largest trading floors in the world has suggested the need to fill what can be viewed as a surprisingly expansive void regarding indicators, statistics, the economic meaning of the associated figures, and the market’s likely reaction.

Those new to the field of investing and economics, including students of the subject, also should benefit from the fundamental, application-oriented nature of this book. As most academics know, if students cannot see the results or directly test theories with practical data, the knowledge they hold tends to remain more theoretical than real-world and they eventually may lose interest in the field. It is here that many future economists are lost. As exercises within an imperfect science, experiments conducted in the social discipline of economics are predominantly theorized or hypothesized and seldom tested with tangible data. In this sense, economists are not as fortunate as physicists or natural scientists, who conduct experiments in a controlled environment such as a laboratory, riverbed, or ocean. The economic indicators contained in these chapters serve as concrete guideposts within the discipline of economics, and as such make experimentation, testing, and study for investments not only possible but understandable.

References

Bartolini, Leonardo, Linda Goldberg, and Adam Sacarny. 2008. “How Economic News Moves Markets.” Federal Reserve Bank of New York Current Issues in Economics and Finance 14, no. 6 (August).

Burns, Arthur F., and Wesley Clair Mitchell. 1946. Measuring Business Cycles. New York: National Bureau of Economic Research.

Cottle, Sidney, Roger F. Murray, and Frank E. Block. 1988. Graham and Dodd’s Security Analysis. New York: McGraw-Hill.

Crescenzi, Anthony. 2002. The Strategic Bond Investor: Strategies and Tools to Unlock the Power of the Bond Market. New York: McGraw-Hill.

Goldberg, Linda, and Deborah Leonard. 2003. “What Moves Sovereign Bond Markets? The Effects of Economic News on U.S. and German Yields.” Federal Reserve Bank of New York Current Issues in Economics and Finance 9, no. 9 (September).

Graham, Benjamin, and David Dodd. 1934. Security Analysis. New York: McGraw-Hill.

Hagenbaugh, Barbara. 2003. “Hints of Optimism Point to Rebound.” USA Today (August 21).

Hager, George. 2001. “Economists Seek Clues in Daily Life.” USA Today (February 12).

Mitchell, Wesley Clair. 1927. Business Cycles: The Problem and Its Setting. New York: National Bureau of Economic Research.

Mui, Yian Q. 2009. “Blue Chip, White Cotton: What Underwear Says About the Economy.” Washington Post (August 31).

National Bureau of Economic Research. 2003. “U.S. Business Cycle Expansions and Contractions.” .

Vance, Julia. 2001. “Titanium Dioxide’s Message.” The Dismal Scientist, Economy.com, April 12. .

Yamarone, Richard. 1999. “The Business Economist at Work: Argus Research Corporation.” Business Economics (July): 65–70.

CHAPTER 1

Gross Domestic Product

Economics has received a bad rap. In the mid-nineteenth century, the great Scottish historian Thomas Carlyle dubbed this discipline “the dismal science,” and jokes abound on Wall Street about economists being more boring than accountants. But truth be told, there is nothing more exciting than watching the newswire on a trading floor of a money-center bank minutes ahead of the release of a major market-moving economic report. One of the top excitement generators is the report on gross domestic product (GDP)—an indicator that is a combination of economics and accounting.

Economists, policy makers, and politicians revere GDP above all other economic statistics because it is the broadest, most comprehensive barometer available of a country’s overall economic condition. GDP is the sum of the market values of all final goods and services produced in a country (that is, domestically) during a specific period using that country’s resources, regardless of the ownership of the resources. For example, all the automobiles made in the United States are included in GDP—even those manufactured in U.S. plants owned by Germany’s BMW and Japan’s Toyota. In contrast, gross national product (GNP) is the sum of the market values of all final goods and services produced by a country’s permanent residents and firms regardless of their location—that is, whether the production occurs domestically or abroad—during a given period. Baked goods produced in Canada by U.S. conglomerate Sara Lee Corporation, for example, are included in U.S. GNP, but not in U.S. GDP.

GDP is a more relevant measure of U.S. economic conditions than GNP, because the resources that are utilized in the production process are predominantly domestic. There are strong parallels between the GDP data and other U.S. economic indicators, such as industrial production and the Conference Board’s index of coincident indicators (the coincident index), which will be explored in later chapters.

The GDP is calculated and reported on a quarterly basis as part of the national income and product accounts (NIPAs). The NIPAs, which were developed and are maintained today by the Commerce Department’s Bureau of Economic Analysis (BEA), are the most comprehensive data available regarding U.S. national output, production, and the distribution of income. Each GDP report contains data on the following:

These data tell the story of how the economy performed—whether it expanded or contracted—during a specific period, usually the preceding quarter. By looking at changes in the GDP’s components and subcomponents and comparing these with changes that have occurred in the past, economists can draw inferences about the direction the economy might take in the future.

Of all the tasks market economists perform, generating a forecast for overall economic performance as measured by the GDP data is the one to which they dedicate the most time. In fact, the latest report on GDP is within arm’s reach of most Wall Street economists. Because several departments in a trading institution rely on the economist’s forecasts, this indicator has emerged as the foundation for all research and trading activity and usually sets the tone for all of Wall Street’s financial prognostications.

Evolution of an Indicator

Measuring a nation’s output and performance is known formally as national income accounting. This process was pioneered largely by Simon Kuznets, an economist hired by the U.S. Department of Commerce in the 1930s—with additional funding from the National Bureau of Economic Research (NBER)—to create an accurate representation of how much the U.S. economy was producing. Up until that time, there was no government agency calculating this most critical of economic statistics.

The initial national income estimates produced by Kuznets in 1934 were representations of income produced, measures of the national economy’s net product, and the national income paid out, or the total compensation for the work performed in the production of net product. At that time, no in-depth breakdown of components existed. In fact, Kuznets didn’t even have a detailed representation of national consumption expenditures. This was the first step of several in the creation of a formal method of national income accounting, yet it was still a far cry from today’s highly detailed representation of the macroeconomy.

The result was the national income and product accounts. In addition to taking on this immense task, Kuznets reconstructed the national income accounts of the United States back to 1869. (He was awarded a Nobel Prize in Economics in 1971 in part for this accomplishment.) Kuznets’s first research report, presented to Congress in 1937, covered national income and output from 1929 through 1935.

In 1947, the first formal presentation of the national income accounts appeared as a supplement to the July issue of the Survey of Current Business. This supplement contained annual data from 1929 to 1946 disseminated in 37 tables. These data were separated into six accounts:

1. National income and product account
2. Business sector income and product account
3. Government receipt/expenditure account
4. Foreign account
5. Personal income/expenditure account
6. Gross savings and investment account

Before the creation of the NIPAs, households, investors, government policy makers, corporations, and economists had little or no information about the complete macroeconomic picture. There were indexes regarding production of raw materials and commodities. There were statistics on prices and government spending. But a comprehensive representation of total economic activity wasn’t available. In fact, the term macroeconomy didn’t appear in print until 1939. Policy making without knowing the past performance of the economy, how it operated under different conditions and scenarios, or which sectors were weak and which were strong, was a daunting task. This may have been the reason for many of the economic-policy failures of the early twentieth century.

Many economists have laid the blame for the Great Depression of the 1930s on the Federal Reserve Board’s failure to respond to the ebullient activity during the Roaring Twenties (sound familiar?). The Fed may have borne much of the responsibility, but very few, if any, have absolved the Federal Reserve of its failures on the grounds that it had insufficient information.

The Great Depression forced the government to develop some sort of national accounting method. World War II furthered the government’s need to understand the nation’s capacity, the composition of its output, and the general economic state of affairs. How could the government possibly plan for war without an accurate appreciation of its resources? Since that time the NIPAs have enabled policy makers to formulate reasonable objectives such as higher economic growth rates or lower inflation rates, as well as to formulate policies to attain these objectives and steer the economy around any roadblocks that might impede the attainment of these goals.

Digging for the Data

Tracking the developments in an economy as large and dynamic as that of the United States is not easy. But through constant revision and upgrading, a relatively small group of dedicated economists at the BEA accomplishes this huge task every quarter. Each quarterly report of economic activity goes through three versions, all available on the BEA web site, . The first, frequently referred to as the advance report, comes one month after the end of the quarter covered, hitting the newswires at 8:30 a.m. (ET). So the GDP report pertaining to the first three months of the year is released sometime during the last week of April, the second quarter’s advance report during the last week of July, the third quarter’s in October, and the fourth quarter’s during the last week of January of the following year. Because not all the data are available during this initial release, the BEA must estimate some series, particularly those involving inventories and foreign trade.

As new data become available, the BEA makes the necessary refinements, deriving a more accurate estimate for GDP. The second release, called the preliminary report, comes two months after the quarter covered—one month after the advance report—and reflects the refinements made to date. The last revision to the data is contained in the final report, which is released three months after the relevant quarter and a month after the preliminary report. The release dates for 2012 are shown in .

 2012 Release Schedule for GDP Reports

Source: U.S. Department of Commerce, Bureau of Economic Analysis

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Annual revisions are calculated during July of every year, based on data that become available to the BEA only on an annual basis, such as state and local government consumption expenditures. The BEA estimates these data on a quarterly basis via a judgmental trend based on annual surveys of state and local governments. Judgmental trends are quarterly interpolations of source data that are available only on an annual basis. Because the surveys are available on an annual basis, estimates can only be made during the annual revision.

As source data for the components of the accounts are continuously updated and revised, the components of the NIPAs must be updated to reflect these revisions. That’s the primary function of the annual revision. Each of the three years’ (12 quarters’) worth of data is subject to revision during this annual updating. Every five years the BEA issues a so-called benchmark revision of all of the data in the NIPAs. This has typically resulted in considerable changes to the five years of quarterly figures.

Benchmark revisions are different from annual revisions in that they generally contain major overhauls to the structure of the report, definitional reclassifications, and new presentations of data. New tables need to be created to account for products that are developed. As the economy evolves, new goods and services come to market and therefore need to be accounted for. Obviously, there were times, for example, when CDs, microwave ovens, DVDs, and iPods didn’t exist. Because the U.S. economy develops and produces these goods, there must be a place for their production to be recorded. All of the data—quarterly and annual—are revised during benchmark revisions.

Some Definitions

As noted previously, GDP is the sum of the market values of all final goods and services produced by the resources (labor and property) of a country residing in that country. This definition contains two particularly important terms: final and produced. When economists refer to final goods, they mean those goods produced for their final intended use, that is, as end products, not as component or intermediate parts in another stage of manufacture. As an example, consider that each year, the Goodyear Tire & Rubber Company produces some hundred million tires. Quite a number of these are created for distribution in retail and wholesale stores as replacements and spares, and these are counted as final goods. And although most tires are produced and delivered to automakers to be used on new automobiles, these are not counted as production, because we do not calculate the value of automobiles in the national accounts by summing the value of its components. In other words, we don’t add the cost of the radio, the seats, the heating elements, the spark plugs, and so on. We count only the value of the final product, the automobile.

Obviously, the economists at the BEA would make a serious miscalculation if they counted all the tires sold by the automakers as part of their automobiles as well as those sold by the manufacturer to Walmart and Sears. The same holds true for the production of wool. BEA economists count only the wool purchased for final use. Because countless final uses exist for wool—sweaters, hats, blankets, and so on—the BEA would make the same double-counting error by adding the production of raw wool as well as the wool used in sweaters, blankets, and the like.

Let’s consider the other important term, produced. Resales are not included in the accounts. Rightly so, the BEA has determined that because the pace of reselling is not indicative of the current pace of production, it shouldn’t be included in the output figures.

Another segment of the economy that the BEA excludes from the GDP release is the activity that goes on off the books. This seems an obvious exclusion, but it’s a big one. Believe it or not, some of the most conservative studies have set the size of the U.S. underground economy at around 10 percent of the official U.S. GDP (roughly $1.5 trillion in the third quarter of 2011). The BEA doesn’t count or make any adjustments for non-state-sanctioned gambling, prostitution, trade in illegal drugs, fraud, the production and sale of counterfeit merchandise, and the like, because, officially, they don’t exist—wink, wink, nudge, nudge. These activities aren’t reported, so how can they be measured? Clandestine activity like this understandably can alter the estimate of several economic indicators, but none more than GDP.

GDP versus GNP

The NIPAs contain figures for both gross domestic product and gross national product. Before 1991, GNP was the benchmark for all economic activity in commentaries, reports, articles, and texts. GDP became the official barometer when the BEA decided that the measure was a better fit with the United Nations system of national accounts used by other nations, and so made international comparisons of economic growth easier.

GDP differs from GNP in what economists call net factor income from foreign sources: the difference between the value of receipts from foreign sources and the payments made to foreign sources. The table in , based on data from the second GDP report of the third quarter of 2011, illustrates how the BEA quantifies this relationship in its GDP report.

 GNP Derived from GDP (QIII 2011 Second Report)

Source: U.S. Department of Commerce, Bureau of Economic Analysis

U.S. GDP $15,180.9 billion
 Plus income receipts from the rest of the world + $794.8 billion
 Minus income payments to the rest of the world $527.8 billion
Equals U.S. GNP = $15,447.9 billion

The difference between the value of GDP and GNP is typically minuscule, usually less than 0.5 percent. In , for example, GDP is approximately $15,181 billion and GNP $15,448 billion, a difference of about $267 billion, or 0.17 percent of GNP.

Calculating GDP: The Aggregate-Expenditure Approach

Every transaction in an economy involves two parties: a buyer and a seller. To calculate total economic activity, economists can focus either on the buyers’ actions, adding together all the expenditures on goods and services, or on the sellers’ actions, tallying the total income received by those employed in the production process. These two approaches correspond to the two methods of calculating the GDP: the aggregate-expenditure method, which is the more popular and the one used on most Wall Street trading floors, and the income approach. The totals reached by both measures should theoretically be the same. In practice, however, there are small differences.

To calculate GDP, the BEA uses the aggregate-expenditure equation:

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where C is personal consumption expenditures, I is gross private domestic investment, G is government consumption expenditures and gross investment, and (X–M ) is the net export value of goods and services (exports minus imports). The identity expressed in this equation is probably the most widely cited of all economic relationships and appears in virtually all introductory macroeconomic texts.

Because the U.S. economy is extremely dynamic and susceptible to sudden and unforeseen influences like inclement weather and war, the percentage of GDP contributed by each of the equation’s components varies over time, even from quarter to quarter. For the most part, though, the proportions don’t deviate significantly from those represented in , which depicts the composition of third quarter 2011 GDP.

 Composition of GDP

Source: U.S. Department of Commerce, Bureau of Economic Analysis

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Personal consumption expenditures (also referred to as consumer spending or simply spending) are the largest component of GDP, accounting for roughly two-thirds of total economic output. During the third quarter of 2011, consumer spending climbed to approximately 71 percent of GDP ($10.798 trillion divided by $15.181 trillion).

Consumer spending is the total market value of household purchases during the accounting term, including items such as beer, telephone service, golf clubs, CDs, gasoline, musical instruments, and taxicab rides. As shown in the table in , these items fall into three categories: durable goods, nondurable goods, and services. Durable goods are those with shelf lives of three or more years. Examples include automobiles, refrigerators, washing machines, televisions, and other big-ticket items such as jewelry, sporting equipment, and guns. Nondurable goods are food, clothing and shoes, energy products such as gasoline and fuel oil, and other items such as tobacco, cosmetics, prescription drugs, magazines, and sundries. Services include housing, household operation, transportation, medical care, and recreation, as well as hairstyling, dry cleaning, funeral services, legal services, and education.

 Consumer Spending Breakdown

Source: U.S. Department of Commerce, Bureau of Economic Analysis

2011: QIII ($ in billions) Percent of Total Spending
Consumer Spending $10,798.7 100.0%
Goods $3,660.1 33.9%
 Durable Goods $1,157.7 10.7%
 Motor vehicles and parts $368.9 3.4%
 Furnishings and durable household equipment $254.8 2.4%
 Recreational goods and vehicles $344.8 3.2%
 Other $189.1 1.8%
 Nondurable Goods $2,502.4 23.2%
 Food and beverages off-premise consumption $815.2 7.5%
 Clothing and footwear $352.0 3.3%
 Gasoline and other energy goods $434.6 4.0%
 Other $900.6 8.3%
Services $7,138.6 66.1%
 Household consumption expenditures $6,855.9 63.5%
 Housing and utilities $1,938.7 18.0%
 Healthcare $1,760.8 16.3%
 Transportation Services $305.1 2.8%
 Recreation services $402.7 3.7%
 Food services and accommodation $685.6 6.3%
 Financial services and insurance $808.3 7.5%
 Other $954.7 8.8%
 Final consumption nonprofits serving households $282.6 2.6%
 Gross output of nonprofits $1,177.0

Services constitute by far the largest category of consumer purchases. They account today for roughly 66 percent of all consumer spending, up from a mere third in 1950. No wonder the United States is said to have a service-based economy. Spending on goods comprises the remaining 34 percent.

Nondurable goods is the second-largest category of expenditures, representing about 23 percent of the total. Durable goods expenditures, the most volatile component, account for the remaining 11 percent.

A more detailed summary of personal consumption expenditures is available on a monthly basis in the BEA’s Personal Income and Outlays report, which is the direct source of data for this component of the GDP report. Personal income and outlays are discussed in Chapter 11.

Gross private domestic investment encompasses spending by businesses (on equipment such as computers, on the construction of factories and production plants, and in mining operations); expenditures on residential housing and apartments; and inventories. Inventories, which consist of the goods businesses produce that remain unsold at the end of a period, are valued by the BEA at the prevailing market price. This value fluctuates greatly from quarter to quarter, making the level of gross private domestic investment quite volatile. Accordingly, economists often look at fixed investment—gross private domestic investment minus inventories. This, in turn, has two major components, residential and nonresidential. The latter, which is also referred to as capital spending, includes expenditures on computers and peripheral equipment, industrial equipment, software, and nonresidential buildings such as plants and factories. The former comprises spending on the construction of new houses and apartment buildings and on related equipment.

Even without the volatile influence of inventories, investment spending is prone to extreme movements, because most of this activity is linked to the ever-changing interest-rate environment. Gross private domestic investment usually accounts for 15 percent of GDP. During the third quarter of 2011, it represented 12.5 percent ($1.895 trillion divided by $15.181 trillion) of GDP.

Government consumption expenditures and gross investment covers all the money laid out by federal, state, and local governments for goods (both durable and nondurable) and services, for both military and nonmilitary purposes. The category includes spending on building and maintaining toll bridges, libraries, parks, highways, and federal office buildings; on compensation for government employees; on research and development, spare parts, food, clothing, ammunition; and on travel, rents, and utilities. Government expenditures and investment usually account for 20 percent of total GDP. During the third quarter of 2011, government consumption expenditures and gross investment did indeed account for about 20 percent of total economic activity ($3.047 trillion divided by $15.181 trillion).

Net exports of goods and services, the last component in the equation, is simply the difference between the dollar value of the goods and services the United States sends abroad (exports) and the dollar value of those it takes in across its borders (imports). Because the country generally imports more than it exports, this figure is usually negative, thus acting as a drag on economic growth. During the third quarter of 2011, net exports subtracted 3.7 percent from total economic activity (–$421.8 billion divided by $15.181 trillion).

Nominal and Real Numbers

The data reported in the GDP release are presented in two forms, nominal and real. Nominal, also known as current-dollar, GDP is the total value, at current prices, of all final goods and services produced during the reporting period. Real, or constant-dollar, GDP is the value of these goods and services using the prices in effect in a specified base year. Economists tend to prefer the real to the nominal measure. To understand why, consider a country that produces only two goods, pencils and vodka—a very interesting economy. If during Year 1, it sells two thousand pencils at $0.10 each and one thousand bottles of vodka at $5.00 a bottle (cheap vodka), its nominal GDP will be $5,200:

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Next year, the same country produces only a thousand pencils and five hundred bottles of vodka but doubles its selling prices, to $0.20 a pencil and $10.00 a bottle. Its nominal GDP is again $5,200:

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Is the economy larger during the second year? Did it produce the same amount? The difficulty in answering these questions illustrates the problem with nominal values. Economists have no way of telling whether it was the price or the quantity produced that increased, or by what magnitude. As more goods and services are considered, the problem gets bigger.

Real GDP is a more accurate indicator of changes in production. Referring to a base year eliminates the uncertainty of whether an increase in the value of the goods and services produced was the result of increased prices or of higher production. The table below shows how real GDP would be calculated in another country with two products—in this case, telescopes and hockey sticks.

To calculate Year 1 GDP, the quantities of the goods produced that year are multiplied by the prices at which they were sold and the results summed, to yield $6,000. For Year 2, instead of multiplying the quantities of goods produced by that year’s prices—which would yield the nominal value—they are multiplied by their prices in the base year, Year 1. This yields a real, or inflation-adjusted, GDP of $7,650. According to this calculation, Year 2 GDP advanced a real $1,650 over Year 1 GDP:

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Until 1996, the BEA used 1982 as the base year for calculating all real GDP estimates. Settling on one base year in this manner has the effect of imposing that year’s price structure on subsequent periods and fixing the relative weights given the goods associated with these prices in the GDP calculation. The BEA found, however, that this fixed-weight approach introduced distortions: The further away a period under study was from the chosen base year, the more inflated its real GDP growth rate tended to be. For example, Karl Whelan, an economist at the Federal Reserve Board, has observed in a working paper that the growth rate of fixed-weight real GDP in 1998 was 4.5 percent when calculated using a base year of 1995, 6.5 percent using 1990 prices, 18.8 percent using 1980 prices, and an incredible 37.4 percent when 1970 is the base year.